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Updated: Jun 6, 2026

An Automated Culture System for Use in Preclinical Testing of Host-Directed Therapies for Tuberculosis

An Automated Culture System for Use in Preclinical Testing of Host-Directed Therapies for Tuberculosis

Published on: August 16, 2021

Transformer-based ensemble framework for tuberculosis treatment response prediction: multi-omics integration with

Huanhuan Ba¹, Huanqing Liu², Tingting Li³

¹Department of Infectious Diseases, The Eighth's Hospital of Xi'an, Xi'an, Shaanxi, China.

Frontiers in Molecular Biosciences

|June 5, 2026

Abstract

Background:

Tuberculosis (TB) treatment response prediction is critical for personalized care. We developed an ensemble Transformer framework integrating clinical and transcriptomic data from 467 patients and five GEO datasets (GSE83456, GSE107995, GSE158802, GSE19435, GSE25534).

Methods:

Five diverse Transformer architectures were trained with Focal Loss, label smoothing, and stratified 5-fold cross-validation. Outcome labels were harmonized using sputum culture conversion at 2 months.

Results:

The model achieved 97.1% accuracy and AUC 0.949 [95% CI: 0.912-0.978] on independent test folds, with sensitivity and specificity both 95%. Bootstrap validation (n = 1000) confirmed robustness.

Conclusion:

This framework provides a clinically-relevant tool for TB treatment response prediction, pending prospective validation.

Clinical Trial Registration:

https://www.chictr.org.cn/, identifier ChiCTR2300074328 03/08/2023.

Keywords:

deep learning ensemble learning multi-omics integration transformer treatment response prediction tuberculosis

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